Introduction: Human‑machine interfaces (HMIs) can improve the quality of life for physically
disabled users. This study proposes a noninvasive BMI design methodology to control a robot
arm using MEG signals acquired during the user's imagined wrist movements in four directions.
Methods: The BMI uses the partial directed coherence measure and a time-varying multivariate
adaptive autoregressive model to extract task-dependent features for mental task discrimination.
An extreme learning machine is used to generate a model with the extracted features, which is
used to control the robot arm for rehabilitation or assistance tasks for motor-impaired individuals.
Results: The classification results show that the proposed BMI methodology is a feasible solution
with good performance and fast learning speed. Discussion: The proposed BMI methodology is a
promising solution for rehabilitation or assistance systems for motor-impaired individuals. The BMI
provides satisfactory classification performance at a fast learning speed.
Keywords: Brain–machine interface, extreme learning machine, functional.